==Call for Papers: NIPS 2009 Workshop on Transfer Learning for
Structured Data (TLSD-09)==
in conjunction with NIPS 2009, Dec 7-12, 2009, Vancouver, B.C., Canada

http://www.cse.ust.hk/~sinnopan/nips09tlsd/

Description and background
------------------------
Recently, transfer learning (TL) has gained much popularity as an
approach to reduce the training-data calibration effort as well as
improve generalization performance of learning tasks. Unlike
traditional learning, transfer learning methods make the best use of
data from one or more source tasks in order to learn a target task.
Many previous works on transfer learning have focused on transferring
the knowledge across domains where the data are assumed to be i.i.d.
In many real-world applications, such as identifying entities in
social networks or classifying Web pages, data are often intrinsically
non i.i.d., which present a major challenge to transfer learning. In
this workshop, we call for papers on the topic of transfer learning
for structured data. Structured data are those that have certain
intrinsic structures such as network topology, and present several
challenges to knowledge transfer. A first challenge is how to judge
the relatedness between tasks and avoid negative transfer. Since data
are non i.i.d., standard methods for measuring the distance between
data distributions, such as KL divergence, Maximum Mean Discrepancy
(MMD) and A-distance, may not be applicable. A second challenge is
that the target and source data may be heterogeneous. For example, a
source domain is a bioinformatics network, while a target domain may
be a network of webpage. In this case, deep transfer or heterogeneous
transfer approaches are required.

Heterogeneous transfer learning for structured data is a new area of
research, which concerns transferring knowledge between different
tasks where the data are non-i.i.d. and may be even heterogeneous.
This area has emerged as one of the most promising areas in machine
learning. In this workshop, we wish to boost the research activities
of knowledge transfer across structured data in the machine learning
community. We welcome theoretical and applied disseminations that make
efforts (1) to expose novel knowledge transfer methodology and
frameworks for transfer mining across structured data. (2) to
investigate effective (automated, human-machined-cooperated)
principles and techniques for acquiring, representing, modeling and
engaging transfer learning on structured data in real-world
applications.

Goals
---------------
This workshop on Transfer learning for structured data will bring
active researchers in artificial intelligence, machine learning and
data mining together toward developing methods or systems together, to
explore methods for solving real-world problems associated with
learning on structured data. The workshop invites researchers
interested in transfer learning, statistical relational learning and
structured data mining to contribute their recent works on the topic
of interest.

Topics of Interest
------------------------
(The topics of interest include but are not limited to the following)

Transfer learning for networked data.
Transfer learning for social networks.
Transfer learning for relational domains.
Transfer learning for non-i.i.d. and/or heterogeneous data.
Transfer learning from multiple structured data sources.
Transfer learning for bioinformatics networks.
Transfer learning for sensor networks.
Theoretical analysis on transfer learning algorithms for structured data.

Paper submission
-----------------------
We encourage authors submit extended abstracts for the preliminary
work up to 4 pages. To encourage the best work in this field can be
presented here, we also allow authors to submit their published or
submitted work up to 9 pages.  Submissions should be using NIPS style
files (available at http://nips.cc/PaperInformation/StyleFiles), and
should include the title, authors' names, institutions and email
addresses, and a brief abstract. Accepted papers will be either
presented as a talk or poster (with poster spotlight). Details of
submission instructions are available at
http://www.cse.ust.hk/~sinnopan/nips09tlsd/ .

Important Dates
------------------------
Deadline for submissions:                October 26, 2009
Notification of acceptance:               November 9, 2009
Deadline for Camera-ready Version:  November 26, 2009
Workshop Date:                              December 12, 2009 (Saturday)

Invited Speakers (Confirmed)
------------------------
Arthur Gretton, Carnegie Mellon University, USA
Shai Ben-David, University of Waterloo, Canada

Workshop Co-chairs
------------------------
Sinno Jialin Pan, Hong Kong University of Science and Technology, Hong Kong
Ivor W. Tsang, Nanyang Technological University, Singapore
Le Song, Carnegie Mellon University, USA
Karsten Borgwardt, MPI for Biological Cybernetics, Germany
Qiang Yang, Hong Kong University of Science and Technology, Hong Kong

Program Committee
------------------------
Andreas Argyriou, Toyota Technological Institute at Chicago, USA
Shai Ben-David, University of Waterloo, Canada
John Blitzer, University of California, USA
Hal Daume III, University of Utah, USA
Jesse Davis, University of Washington, USA
Jing Gao, University of Illinois, Urbana-Champaign, USA
Steven Hoi, Nanyang Technological University, Singapore
Jing Jiang, Singapore Management University, Singapore
Honglak Lee, Stanford University, USA
Lily Mihalkova, University of Maryland, USA
Raymond Mooney, University of Texas at Austin, USA
Massimiliano Pontil, University College London, UK
Masashi Sugiyama, Tokyo Institute of Technology, Japan
Koji Tsuda, AIST Computational Biology Research Center, Japan
Jingdong Wang, Microsoft Research Asia, China
Dong Xu, Nanyang Technological University, Singapore

If you have any questions, please contact us via tlsd09n...@gmail.com.

http://groups.google.com/group/mlchina?hl=zh-CN